DOCSLIB.ORG

Scelestial: Fast and Accurate Single-Cell Lineage Tree Inference Based on a Steiner Tree Approximation Algorithm M.-H

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Scelestial: Fast and Accurate Single-Cell Lineage Tree Inference Based on a Steiner Tree Approximation Algorithm M.-H bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445405; this version posted May 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Scelestial: fast and accurate single-cell lineage tree inference based on a Steiner tree approximation algorithm M.-H. Foroughmand-Araabi1, S. Goliaei1, A. C. McHardy1* 1 Department of Computational Biology of Infection Research, Helmholtz Centre for Infection Research, Braunschweig, Germany * Corresponding author ([email protected]) Abstract Single-cell genome sequencing provides a highly granular view of biological systems but is affected by high error rates, allelic amplification bias, and uneven genome coverage. This creates a need for data-specific computational methods, for purposes such as for cell lineage tree inference. The objective of cell lineage tree reconstruction is to infer the evolutionary process that generated a set of observed cell genomes. Lineage trees may enable a better understanding of tumor formation and growth, as well as of organ development for healthy body cells. We describe a method, Scelestial, for lineage tree reconstruction from single-cell data, which is based on an approximation algorithm for the Steiner tree problem and is a generalization of the neighbor-joining method. We adapt the algorithm to efficiently select a limited subset of potential sequences as internal nodes, in the presence of missing values, and to minimize cost by lineage tree-based missing value imputation. In a comparison against seven state-of-the-art single-cell lineage tree reconstruction algorithms - BitPhylogeny, OncoNEM, SCITE, SiFit, SASC, SCIPhI, and SiCloneFit - on simulated and real single-cell tumor samples, Scelestial performed best at reconstructing trees in terms of accuracy and run time. Scelestial has been implemented in C++. It is also available as an R package named RScelestial. May 12, 2021 1/39 bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445405; this version posted May 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 1 Introduction Lineage trees describe the evolutionary process that created a sample of clonally related entities, such as individual cells within an organ or a tumor. A lineage tree also suggests the constitution of a founder cell, as well as of its descendants, and the evolutionary events that occurred during lin- eage formation. Single-cell genomic data provide a highly resolved view of cellular evolution, substantially more than bulk genome sequencing [1]. However, they also come with high rates of missing values and distorted allele frequencies arising from amplification biases and sequencing errors [2]. Lineage tree reconstruction from single-cell data therefore requires specific considerations for handling missing values and errors. Specific approaches to tackle this problem include the methods of Kim and Simon [3], Bit- Phylogeny [4], OncoNEM [5], SCITE [6], SiFit [7], SASC [8], SPhyR [9], SCIPhI [10], SiCloneFit [11], B-SCITE [12], and PhISCS [13]. Kim and Simon [3] make use of the infinite site assumption and infer a \mutation tree" based on calculating a probability for ordering mutations in a lineage and constructing a tree, finding the maximum spanning tree in this graph. BitPhylogeny [4] provides a stochastic process through a graphical model that stochastically generates the given input data and uses Markov Chain Monte Carlo (MCMC) for sampling to search for the best lineage tree model and the associated parameters. OncoNEM [5] and SCITE [6] infer a phylogenetic tree over all the samples under a maximum likelihood model. OncoNEM uses a heuristic search, whereas SCITE uses MCMC sampling to find a maximum likelihood tree. SiCloneFit [11] infers subclonal structures and a phylogeny via a Bayesian method under the finite site assumption. SASC [8] and SPhyR [9] consider the k-Dollo model, a more relaxed model in comparison than infinite site assumption. In k-Dollo model, a mutation can be gained once in a tumor but may be lost multiple times afterwards. SASC uses simulated annealing and SPhyR uses k-means to find the best k-Dollo evolutionary tree. B-SCITE [12] and PhISCS [13] infer subclonal evolution, based on a combination of single-cell and bulk sequencing data. May 12, 2021 2/39 bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445405; this version posted May 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. B-SCITE uses an MCMC method to maximize a likelihood function for trees and sequencing data. PhISCS formulates tree reconstruction as com- binatorial and mathematical programming problems, and uses standard mathematical programming solvers to find the solution. Integer or inte- ger linear programmings for the phylogenetic tree reconstruction are also available [14,15]. The Steiner tree problem is a classic problem in theoretical computer science with a wide range of applications in various areas including very- large-scale integration design [16], network routing [17], civil engineering [18], and other areas [19{22]. Given a weighted graph and some vertices marked as terminals, the Steiner tree problem is the problem of finding the minimum weighted tree that connects all the terminal vertices, with non-terminal vertices that may or may not be included in the optimal tree. Although the Steiner tree problem is NP-hard and no polynomial-time exact solution is known, some elegant approximation algorithms are available. They guarantee polynomial time and a constant factor approximation ratio, and have been used for phylogenetic reconstruction [23]. This is a great advantage compared with sampling heuristics such as MCMC for finding an optimal solution, which may be trapped in local optima. Here, we describe Scelestial, a method for lineage tree reconstruction from single-cell datasets, based on the Berman approximation algorithm for the Steiner tree problem [24]. Our method infers the evolutionary history for single-cell data in the form of a lineage tree and imputes the missing values accordingly. Dealing with missing values makes the problem much harder in theory. To overcome this difficulty, we represent a hypercube represented via [24] as a sequence with missing values as its center. This representation, although imposes some cost in the results of the algorithm caused by misplacement of the center from the best imputation, which we do not know in advance, helps us to get a fast yet accurate and robust algorithm. May 12, 2021 3/39 bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445405; this version posted May 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. 2 Results 2.1 Performance in lineage tree reconstruction on sim- ulated data We compared the performance of Scelestial to SCITE, OncoNEM, BitPhy- logeny, SASC (as a recent instance of k-Dollo-based methods), SCIPhI, SiFit, and SiCloneFit. For this, we generated data with the cell evolution simulator provided by OncoNEM and with another tumor evolution sim- ulator that we developed (Section 3.2). To assess the inferred trees, we calculated their distance to the ground truth lineage trees as the normalized pairwise distances between corresponding samples, as described in Sec- tion 3.4.1. In addition, we calculated the similarity between the generated trees and the ground truth by comparing tree splits (Section 3.4.2). For run time evaluation, we executed all the methods on simulated tumor data over a range of numbers of samples and sites. We also assessed the run time performance of the methods in relation to different parameters, such as the number of samples and sites (Section 2.5). First, we evaluated the algorithms on data created with OncoNEM's simulator. The simulator creates samples by producing clones and sampling from these clones (Section 3.3). It accepts the number of clones (i.e., the number of nodes in the lineage tree), the number of sampled cells, the number of sites, the false positive rate, the false negative rate, and the missing value rate as parameters. We used 1.5% as the setting for the false positive positives, 10% for false negatives and 7% for the missing value rate. These parameters are consistent with observed parameters in single-cell data [25]. We performed tests for 50 and 100 samples, 5 and 10 clones, and 20 and 50 sites, and calculated the pairwise distance between the inferred and ground truth trees for all methods (Table 1). From these data, Scelestial reconstructed lineage trees with the lowest distance to the ground truth tree in 5 out of 8 cases. SASC, SCITE, and SiFit each performed best in one of the remaining cases. Next, we simulated 100 single-cell datasets from a solid tumor covering May 12, 2021 4/39 bioRxiv preprint doi: https://doi.org/10.1101/2021.05.24.445405; this version posted May 24, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Table 1: Comparison of reconstruction of ground truth lineage tree from data simulated by OncoNEM, showing the distance between the inferred trees and the ground truth for all methods across eight lineage trees.
Load more

Recommended publications
  • Species Concepts Should Not Conflict with Evolutionary History, but Often Do
    ARTICLE IN PRESS Stud. Hist. Phil. Biol. & Biomed. Sci. xxx (2008) xxx–xxx Contents lists available at ScienceDirect Stud. Hist. Phil. Biol. & Biomed. Sci. journal homepage: www.elsevier.com/locate/shpsc Species concepts should not conflict with evolutionary history, but often do Joel D. Velasco Department of Philosophy, University of Wisconsin-Madison, 5185 White Hall, 600 North Park St., Madison, WI 53719, USA Department of Philosophy, Building 90, Stanford University, Stanford, CA 94305, USA article info abstract Keywords: Many phylogenetic systematists have criticized the Biological Species Concept (BSC) because it distorts Biological Species Concept evolutionary history. While defences against this particular criticism have been attempted, I argue that Phylogenetic Species Concept these responses are unsuccessful. In addition, I argue that the source of this problem leads to previously Phylogenetic Trees unappreciated, and deeper, fatal objections. These objections to the BSC also straightforwardly apply to Taxonomy other species concepts that are not defined by genealogical history. What is missing from many previous discussions is the fact that the Tree of Life, which represents phylogenetic history, is independent of our choice of species concept. Some species concepts are consistent with species having unique positions on the Tree while others, including the BSC, are not. Since representing history is of primary importance in evolutionary biology, these problems lead to the conclusion that the BSC, along with many other species concepts, are unacceptable. If species are to be taxa used in phylogenetic inferences, we need a history- based species concept. Ó 2008 Elsevier Ltd. All rights reserved. When citing this paper, please use the full journal title Studies in History and Philosophy of Biological and Biomedical Sciences 1.
    [Show full text]
  • Phylogenetic Comparative Methods: a User's Guide for Paleontologists
    Phylogenetic Comparative Methods: A User’s Guide for Paleontologists Laura C. Soul - Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA David F. Wright - Division of Paleontology, American Museum of Natural History, Central Park West at 79th Street, New York, New York 10024, USA and Department of Paleobiology, National Museum of Natural History, Smithsonian Institution, Washington, DC, USA Abstract. Recent advances in statistical approaches called Phylogenetic Comparative Methods (PCMs) have provided paleontologists with a powerful set of analytical tools for investigating evolutionary tempo and mode in fossil lineages. However, attempts to integrate PCMs with fossil data often present workers with practical challenges or unfamiliar literature. In this paper, we present guides to the theory behind, and application of, PCMs with fossil taxa. Based on an empirical dataset of Paleozoic crinoids, we present example analyses to illustrate common applications of PCMs to fossil data, including investigating patterns of correlated trait evolution, and macroevolutionary models of morphological change. We emphasize the importance of accounting for sources of uncertainty, and discuss how to evaluate model fit and adequacy. Finally, we discuss several promising methods for modelling heterogenous evolutionary dynamics with fossil phylogenies. Integrating phylogeny-based approaches with the fossil record provides a rigorous, quantitative perspective to understanding key patterns in the history of life. 1. Introduction A fundamental prediction of biological evolution is that a species will most commonly share many characteristics with lineages from which it has recently diverged, and fewer characteristics with lineages from which it diverged further in the past. This principle, which results from descent with modification, is one of the most basic in biology (Darwin 1859).
    [Show full text]
  • Lineages, Splits and Divergence Challenge Whether the Terms Anagenesis and Cladogenesis Are Necessary
    Biological Journal of the Linnean Society, 2015, , – . With 2 figures. Lineages, splits and divergence challenge whether the terms anagenesis and cladogenesis are necessary FELIX VAUX*, STEVEN A. TREWICK and MARY MORGAN-RICHARDS Ecology Group, Institute of Agriculture and Environment, Massey University, Palmerston North, New Zealand Received 3 June 2015; revised 22 July 2015; accepted for publication 22 July 2015 Using the framework of evolutionary lineages to separate the process of evolution and classification of species, we observe that ‘anagenesis’ and ‘cladogenesis’ are unnecessary terms. The terms have changed significantly in meaning over time, and current usage is inconsistent and vague across many different disciplines. The most popular definition of cladogenesis is the splitting of evolutionary lineages (cessation of gene flow), whereas anagenesis is evolutionary change between splits. Cladogenesis (and lineage-splitting) is also regularly made synonymous with speciation. This definition is misleading as lineage-splitting is prolific during evolution and because palaeontological studies provide no direct estimate of gene flow. The terms also fail to incorporate speciation without being arbitrary or relative, and the focus upon lineage-splitting ignores the importance of divergence, hybridization, extinction and informative value (i.e. what is helpful to describe as a taxon) for species classification. We conclude and demonstrate that evolution and species diversity can be considered with greater clarity using simpler, more transparent terms than anagenesis and cladogenesis. Describing evolution and taxonomic classification can be straightforward, and there is no need to ‘make words mean so many different things’. © 2015 The Linnean Society of London, Biological Journal of the Linnean Society, 2015, 00, 000–000.
    [Show full text]
  • Statistical Inference for the Linguistic and Non-Linguistic Past
    Statistical inference for the linguistic and non-linguistic past Igor Yanovich DFG Center for Advanced Study “Words, Bones, Genes and Tools” Universität Tübingen July 6, 2017 Igor Yanovich 1 / 46 Overview of the course Overview of the course 1 Today: trees, as a description and as a process 2 Classes 2-3: simple inference of language-family trees 3 Classes 4-5: computational statistical inference of trees and evolutionary parameters 4 Class 6: histories of languages and of genes 5 Class 7: simple spatial statistics 6 Class 8: regression taking into account linguistic relationships; synthesis of the course Igor Yanovich 2 / 46 Overview of the course Learning outcomes By the end of the course, you should be able to: 1 read and engage with the current literature in linguistic phylogenetics and in spatial statistics for linguistics 2 run phylogenetic and basic spatial analyses on linguistic data 3 proceed further in the subject matter on your own, towards further advances in the field Igor Yanovich 3 / 46 Overview of the course Today’s class 1 Overview of the course 2 Language families and their structures 3 Trees as classifications and as process depictions 4 Linguistic phylogenetics 5 Worries about phylogenetics in linguistics vs. biology 6 Quick overview of the homework 7 Summary of Class 1 Igor Yanovich 4 / 46 Language families and their structures Language families and their structures Igor Yanovich 5 / 46 Language families and their structures Dravidian A modification of [Krishnamurti, 2003, Map 1.1], from Wikipedia Igor Yanovich 6 / 46 Language families and their structures Dravidian [Krishnamurti, 2003]’s classification: Dravidian family South Dravidian Central Dravidian North Dravidian SD I SD II Tamil Malay¯al.am Kannad.a Telugu Kolami Brahui (70M) (38M) (40M) (75M) (0.1M) (4M) (Numbers of speakers from Wikipedia) Igor Yanovich 7 / 46 Language families and their structures Dravidian: similarities and differences Proto-Dr.
    [Show full text]
  • Learning the “Game” of Life: Reconstruction of Cell Lineages Through Crispr-Induced Dna Mutations
    LEARNING THE “GAME” OF LIFE: RECONSTRUCTION OF CELL LINEAGES THROUGH CRISPR-INDUCED DNA MUTATIONS JIAXIAO CAI, DA KUANG, ARUN KIRUBARAJAN, MUKUND VENKATESWARAN ABSTRACT. Multi-cellular organisms are composed of many billions of individuals cells that exist by the mutation of a single stem cell. The CRISPR-based molecular-tools induce DNA mutations, which allows us to study the mutation lineages of various multi-ceulluar organisms. However, to date no lineage reconstruction algorithms have been examined for their performance/robustness across various molecular tools, datasets and sizes of lineage trees. In addition, it is unclear whether classical machine learning algorithms, deep neural networks or some combination of the two are the best approach for this task. In this paper, we introduce 1) a simulation framework for the zygote development process to achieve a dataset size required by deep learning models, and 2) various supervised and unsupervised approaches for cell mutation tree reconstruction. In particular, we show how deep generative models (Autoencoders and Variational Autoencoders), unsupervised clustering methods (K-means), and classical tree reconstruction algorithms (UPGMA) can all be used to trace cell mutation lineages. 1. INTRODUCTION Multi-cellular organisms are composed of billions or trillions of different interconnected cells that derive from a single cell through repeated rounds of cell division — knowing the cell lineage that produces a fully developed organism from a single cell provides the framework for understanding when, where, and how cell fate decisions are made. The recent advent of new CRISPR-based molecular tools synthetically induced DNA mutations and made it possible to solve lineage of complex model organisms at single-cell resolution.[1] Starting in early embryogenesis, CRISPR-induced mutations occur stochastically at introduced target sites, and these mutations are stably inherited by the offspring of these cells and immune to further change.
    [Show full text]
  • Phylogenetics
    Phylogenetics What is phylogenetics? • Study of branching patterns of descent among lineages • Lineages – Populations – Species – Molecules • Shift between population genetics and phylogenetics is often the species boundary – Distantly related populations also show patterning – Patterning across geography What is phylogenetics? • Goal: Determine and describe the evolutionary relationships among lineages – Order of events – Timing of events • Visualization: Phylogenetic trees – Graph – No cycles Phylogenetic trees • Nodes – Terminal – Internal – Degree • Branches • Topology Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ Stephan Steigele Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ Phylogenetic trees • Rooted or unrooted – Rooted: Precisely 1 internal node of degree 2 • Node that represents the common ancestor of all taxa – Unrooted: All internal nodes with degree 3+ • Binary: all speciation events produce two lineages from one • Cladogram: Topology only • Phylogram: Topology with edge lengths representing time or distance • Ultrametric: Rooted tree with time-based edge lengths (all leaves equidistant from root) Phylogenetic trees • Clade: Group of ancestral and descendant lineages • Monophyly: All of the descendants of a unique common ancestor • Polyphyly:
    [Show full text]
  • Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization
    Advances in Computational Methods for Phylogenetic Networks in the Presence of Hybridization R. A. Leo Elworth, Huw A. Ogilvie, Jiafan Zhu, and Luay Nakhleh Abstract Phylogenetic networks extend phylogenetic trees to allow for modeling reticulate evolutionary processes such as hybridization. They take the shape of a rooted, directed, acyclic graph, and when parameterized with evolutionary parame- ters, such as divergence times and population sizes, they form a generative process of molecular sequence evolution. Early work on computational methods for phylo- genetic network inference focused exclusively on reticulations and sought networks with the fewest number of reticulations to fit the data. As processes such as incom- plete lineage sorting (ILS) could be at play concurrently with hybridization, work in the last decade has shifted to computational approaches for phylogenetic net- work inference in the presence of ILS. In such a short period, significant advances have been made on developing and implementing such computational approaches. In particular, parsimony, likelihood, and Bayesian methods have been devised for estimating phylogenetic networks and associated parameters using estimated gene trees as data. Use of those inference methods has been augmented with statistical tests for specific hypotheses of hybridization, like the D-statistic. Most recently, Bayesian approaches for inferring phylogenetic networks directly from sequence data were developed and implemented. In this chapter, we survey such advances and discuss model assumptions as well as methods’ strengths and limitations. We also discuss parallel efforts in the population genetics community aimed at inferring similar structures. Finally, we highlight major directions for future research in this area. R. A. Leo Elworth Rice University e-mail: [email protected] Huw A.
    [Show full text]
  • Reading Phylogenetic Trees: a Quick Review (Adapted from Evolution.Berkeley.Edu)
    Biological Trees Gloria Rendon SC11 Education June, 2011 Biological trees • Biological trees are used for the purpose of classification, i.e. grouping and categorization of organisms by biological type such as genus or species. Types of Biological trees • Taxonomy trees, like the one hosted at NCBI, are hierarchies; thus classification is determined by position or rank within the hierarchy. It goes from kingdom to species. • Phylogenetic trees represent evolutionary relationships, or genealogy, among species. Nowadays, these trees are usually constructed by comparing 16s/18s ribosomal RNA. • Gene trees represent evolutionary relationships of a particular biological molecule (gene or protein product) among species. They may or may not match the species genealogy. Examples: hemoglobin tree, kinase tree, etc. TAXONOMY TREES Exercise 1: Exploring the Species Tree at NCBI •There exist many taxonomies. •In this exercise, we will examine the taxonomy at NCBI. •NCBI has a taxonomy database where each category in the tree (from the root to the species level) has a unique identifier called taxid. •The lineage of a species is the full path you take in that tree from the root to the point where that species is located. •The (NCBI) taxonomy common tree is therefore the tree that results from adding together the full lineages of each species in a particular list of your choice. Exercise 1: Exploring the Species Tree at NCBI • Open a web browser on NCBI’s Taxonomy page http://www.ncbi.nlm.n ih.gov/Taxonomy/ • Click on each one of the names here to look up the taxonomy id (taxid) of each one of the five categories of the taxonomy browser: Archaea, bacteria, Eukaryotes, Viroids and Viruses.
    [Show full text]
  • Phylogenetic Systematics As the Basis of Comparative Biology
    SMITHSONIAN CONTRIBUTIONS TO BOTANY NUMBER 73 Phylogenetic Systematics as the Basis of Comparative Biology KA. Funk and Daniel R. Brooks SMITHSONIAN INSTITUTION PRESS Washington, D.C. 1990 ABSTRACT Funk, V.A. and Daniel R. Brooks. Phylogenetic Systematics as the Basis of Comparative Biology. Smithsonian Contributions to Botany, number 73, 45 pages, 102 figures, 12 tables, 199O.-Evolution is the unifying concept of biology. The study of evolution can be approached from a within-lineage (microevolution) or among-lineage (macroevolution) perspective. Phylogenetic systematics (cladistics) is the appropriate basis for all among-liieage studies in comparative biology. Phylogenetic systematics enhances studies in comparative biology in many ways. In the study of developmental constraints, the use of such phylogenies allows for the investigation of the possibility that ontogenetic changes (heterochrony) alone may be sufficient to explain the perceived magnitude of phenotypic change. Speciation via hybridization can be suggested, based on the character patterns of phylogenies. Phylogenetic systematics allows one to examine the potential of historical explanations for biogeographic patterns as well as modes of speciation. The historical components of coevolution, along with ecological and behavioral diversification, can be compared to the explanations of adaptation and natural selection. Because of the explanatory capabilities of phylogenetic systematics, studies in comparative biology that are not based on such phylogenies fail to reach their potential. OFFICIAL PUBLICATION DATE is handstamped in a limited number of initial copies and is recorded in the Institution's annual report, Srnithonhn Year. SERIES COVER DESIGN: Leaf clearing from the katsura tree Cercidiphyllumjaponicum Siebold and Zuccarini. Library of Cmgrcss Cataloging-in-PublicationDiaa Funk, V.A (Vicki A.), 1947- PhylogmttiC ryrtcmaticsas tk basis of canpamtive biology / V.A.
    [Show full text]
  • Allozyme Divergence and Phylogenetic Relationships Among Capra, Ovis and Rupicapra (Artyodactyla, Bovidae)
    Heredity'S? (1991) 281—296 Received 28 November 7990 Genetical Society of Great Britain Allozyme divergence and phylogenetic relationships among Capra, Ovis and Rupicapra (Artyodactyla, Bovidae) E. RANOI,* G. FUSCO,* A. LORENZINI,* S. TOSO & G. TOSIt *g7ftj Nez/c nate di 9/clog/a del/a Selvaggine, Via Ca Fornacetta, 9 Ozzano dell'EmiIia (Bo) Italy, and j-Dipartimento di B/Wag/a, Universitâ diM/lana, Via Ce/ar/a, 3 Mi/epa,Italy Geneticdivergence and phylogenetic relationships between the chamois (Rupicaprini, Rupkapra rupicapra rupicapra) and three species of the Caprini (Capra aegagrus hircus, Capra ibex ibex and Ovis amrnon musUnon) have been studied by multilocus protein electrophoresis. Dendrograms have been constructed both with distance and parsimony methods. Goat, sheep and chamois pair- wise genetic distances had very similar values, All the topologies showed that Capra, Ovis and Rupicapra originate from the same internode, suggesting the hypothesis of a common, and almost contemporaneous, ancestor. The estimated divergence times among the three genera ranged from 5.28 to 7.08 Myr. These findings suggest the need to reconsider the evolutionary relationships in the Caprinae. Keywords:allozymes,Caprinae, electrophoresis phylogenetic trees. caprid lineage since the lower or middle Miocene. Introduction ShaDer (1977) agrees with the outline given by Thenius Theevolutionary relationships of the subfamily & Hofer (1960) supporting the idea of a more recent Caprinae (Artyodactyla, Bovidae; Corbet, 1978) have origin of the Caprini, and in particular of a Pliocenic been discussed by Geist (197!) within the framework splitting of Ovis and Capra. In Geist's (1971) opinion of his dispersal theory of Ice Age mammal evolution.
    [Show full text]
  • Taxonomy and Classification Goals: Un Ders Tan D Traditi Onal and Hi Erarchi Cal Cl Assifi Cati Ons of Biodiversity, and What Information Classifications May Contain
    Taxonomy and classification Goals: Un ders tan d tra ditional and hi erarchi cal cl assifi cati ons of biodiversity, and what information classifications may contain. Readings: 1. Chapter 1. Figure 1-1 from Pough et al. Taxonomy and classification (cont ’d) Some new words This is a cladogram. Each branching that are very poiiint is a nod dEhbhe. Each branch, starti ng important: at the node, is a clade. 9 Cladogram 9 Clade 9 Synapomorphy (Shared, derived character) 9 Monophyly; monophyletic 9 PhlParaphyly; parap hlihyletic 9 Polyphyly; polyphyletic Definitions of cladogram on the Web: A dichotomous phylogenetic tree that branches repeatedly, suggesting the classification of molecules or org anisms based on the time sequence in which evolutionary branches arise. xray.bmc.uu.se/~kenth/bioinfo/glossary.html A tree that depicts inferred historical branching relationships among entities. Unless otherwise stated, the depicted branch lengt hs in a cl ad ogram are arbi trary; onl y th e b ranchi ng ord er is significant. See phylogram. www.bcu.ubc.ca/~otto/EvolDisc/Glossary.html TAKE-HOME MESSAGE: Cladograms tell us about the his tory of the re lati onshi ps of organi sms. K ey word : Hi st ory. Historically, classification of organisms was mainlyypg a bookkeeping task. For this monumental job, Carrolus Linnaeus invented the s ystem of binomial nomenclature that we are all familiar with. (Did you know that his name was Carol Linne? He liidhilatinized his own name th e way h e named speci i!)es!) Merely giving species names and arranging them according to similar groups was acceptable while we thought species were static entities .
    [Show full text]
  • Phylogenetic Comparative Methods
    Phylogenetic Comparative Methods Luke J. Harmon 2019-3-15 1 Copyright This is book version 1.4, released 15 March 2019. This book is released under a CC-BY-4.0 license. Anyone is free to share and adapt this work with attribution. ISBN-13: 978-1719584463 2 Acknowledgements Thanks to my lab for inspiring me, my family for being my people, and to the students for always keeping us on our toes. Helpful comments on this book came from many sources, including Arne Moo- ers, Brian O’Meara, Mike Whitlock, Matt Pennell, Rosana Zenil-Ferguson, Bob Thacker, Chelsea Specht, Bob Week, Dave Tank, and dozens of others. Thanks to all. Later editions benefited from feedback from many readers, including Liam Rev- ell, Ole Seehausen, Dean Adams and lab, and many others. Thanks! Keep it coming. If you like my publishing model try it yourself. The book barons are rich enough, anyway. Except where otherwise noted, this book is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit https: //creativecommons.org/licenses/by/4.0/. 3 Table of contents Chapter 1 - A Macroevolutionary Research Program Chapter 2 - Fitting Statistical Models to Data Chapter 3 - Introduction to Brownian Motion Chapter 4 - Fitting Brownian Motion Chapter 5 - Multivariate Brownian Motion Chapter 6 - Beyond Brownian Motion Chapter 7 - Models of discrete character evolution Chapter 8 - Fitting models of discrete character evolution Chapter 9 - Beyond the Mk model Chapter 10 - Introduction to birth-death models Chapter 11 - Fitting birth-death models Chapter 12 - Beyond birth-death models Chapter 13 - Characters and diversification rates Chapter 14 - Summary 4 Chapter 1: A Macroevolutionary Research Pro- gram Section 1.1: Introduction Evolution is happening all around us.
    [Show full text]